library(Seurat)
mat <- list()
mat$HC1 <- Read10X_h5(filename = "../data/COVID-19/GSM4475048_C51_filtered_feature_bc_matrix.h5")
mat$HC2 <- Read10X_h5(filename = "../data/COVID-19/GSM4475049_C52_filtered_feature_bc_matrix.h5")
mat$HC3 <- Read10X_h5(filename = "../data/COVID-19/GSM4475050_C100_filtered_feature_bc_matrix.h5")
mat$HC4 <- Read10X(data.dir = "../data/COVID-19/GSM3660650/")
mat$M1 <- Read10X_h5(filename = "../data/COVID-19/GSM4339769_C141_filtered_feature_bc_matrix.h5")
mat$M2 <- Read10X_h5(filename = "../data/COVID-19/GSM4339770_C142_filtered_feature_bc_matrix.h5")
mat$M3 <- Read10X_h5(filename = "../data/COVID-19/GSM4339772_C144_filtered_feature_bc_matrix.h5")
mat$S1 <- Read10X_h5(filename = "../data/COVID-19/GSM4339773_C145_filtered_feature_bc_matrix.h5")
mat$S2 <- Read10X_h5(filename = "../data/COVID-19/GSM4339771_C143_filtered_feature_bc_matrix.h5")
mat$S3 <- Read10X_h5(filename = "../data/COVID-19/GSM4339774_C146_filtered_feature_bc_matrix.h5")
mat$S4 <- Read10X_h5(filename = "../data/COVID-19/GSM4475051_C148_filtered_feature_bc_matrix.h5")
mat$S5 <- Read10X_h5(filename = "../data/COVID-19/GSM4475052_C149_filtered_feature_bc_matrix.h5")
mat$S6 <- Read10X_h5(filename = "../data/COVID-19/GSM4475053_C152_filtered_feature_bc_matrix.h5")
meta.data <- read.delim("../data/COVID-19/all.cell.annotation.meta.txt")
rownames(meta.data) <- meta.data$ID
head(meta.data)
numbering = c(HC1 = 1,
HC2 = 2,
HC3 = 3,
HC4 = 4,
M1 = 5,
M2 = 6,
M3 = 7,
S1 = 9,
S2 = 8,
S3 = 10,
S4 = 11,
S5 = 12,
S6 = 13)
for (i in names(mat)){
colnames(mat[[i]]) <- gsub('-', '_', colnames(mat[[i]]))
colnames(mat[[i]]) <- gsub('1', numbering[i], colnames(mat[[i]]))
}
for (i in names(mat)){
mat[[i]] <- mat[[i]][, meta.data$ID[meta.data$sample_new == i]]
}
objs <- list()
for (i in names(mat)){
objs[[i]] <- CreateSeuratObject(mat[[i]], project = i, meta.data = meta.data[meta.data$sample_new == i, ])
}
for (i in names(objs)) {
objs[[i]] <- NormalizeData(objs[[i]], verbose=FALSE)
objs[[i]] <- FindVariableFeatures(objs[[i]], selection.method = "vst", nfeatures = 2000, verbose=FALSE)
objs[[i]] <- ScaleData(objs[[i]], verbose=FALSE)
objs[[i]] <- RunPCA(objs[[i]], features = VariableFeatures(object = objs[[i]]), verbose=FALSE)
}
ptm = proc.time()
anchors <- FindIntegrationAnchors(object.list = objs, dims = 1:30)
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|++++ | 8 % ~11s
|++++++++ | 15% ~09s
|++++++++++++ | 23% ~06s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++++++ | 46% ~03s
|+++++++++++++++++++++++++++ | 54% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Finding all pairwise anchors
| | 0 % ~calculating
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 22833 anchors
Filtering anchors
Retained 4260 anchors
Extracting within-dataset neighbors
|+ | 1 % ~03h 26m 22s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 9322 anchors
Filtering anchors
Retained 3843 anchors
Extracting within-dataset neighbors
|++ | 3 % ~02h 14m 26s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 9570 anchors
Filtering anchors
Retained 3148 anchors
Extracting within-dataset neighbors
|++ | 4 % ~01h 48m 33s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 8829 anchors
Filtering anchors
Retained 2084 anchors
Extracting within-dataset neighbors
|+++ | 5 % ~01h 37m 40s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 9093 anchors
Filtering anchors
Retained 1633 anchors
Extracting within-dataset neighbors
|++++ | 6 % ~01h 30m 22s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6187 anchors
Filtering anchors
Retained 2540 anchors
Extracting within-dataset neighbors
|++++ | 8 % ~01h 17m 33s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 10101 anchors
Filtering anchors
Retained 2688 anchors
Extracting within-dataset neighbors
|+++++ | 9 % ~01h 17m 53s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 11136 anchors
Filtering anchors
Retained 2485 anchors
Extracting within-dataset neighbors
|++++++ | 10% ~01h 17m 19s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7221 anchors
Filtering anchors
Retained 2977 anchors
Extracting within-dataset neighbors
|++++++ | 12% ~01h 10m 30s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5225 anchors
Filtering anchors
Retained 2148 anchors
Extracting within-dataset neighbors
|+++++++ | 13% ~01h 05m 20s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 10004 anchors
Filtering anchors
Retained 2725 anchors
Extracting within-dataset neighbors
|++++++++ | 14% ~01h 05m 30s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 11019 anchors
Filtering anchors
Retained 2878 anchors
Extracting within-dataset neighbors
|++++++++ | 15% ~01h 05m 16s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7098 anchors
Filtering anchors
Retained 3215 anchors
Extracting within-dataset neighbors
|+++++++++ | 17% ~01h 01m 05s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5410 anchors
Filtering anchors
Retained 2697 anchors
Extracting within-dataset neighbors
|+++++++++ | 18% ~57m 42s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 8838 anchors
Filtering anchors
Retained 6318 anchors
Extracting within-dataset neighbors
|++++++++++ | 19% ~55m 10s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1686 anchors
Filtering anchors
Retained 1169 anchors
Extracting within-dataset neighbors
|+++++++++++ | 21% ~51m 50s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1625 anchors
Filtering anchors
Retained 1152 anchors
Extracting within-dataset neighbors
|+++++++++++ | 22% ~48m 46s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1551 anchors
Filtering anchors
Retained 1409 anchors
Extracting within-dataset neighbors
|++++++++++++ | 23% ~45m 31s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1659 anchors
Filtering anchors
Retained 1582 anchors
Extracting within-dataset neighbors
|+++++++++++++ | 24% ~42m 36s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1584 anchors
Filtering anchors
Retained 1561 anchors
Extracting within-dataset neighbors
|+++++++++++++ | 26% ~40m 02s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1583 anchors
Filtering anchors
Retained 1570 anchors
Extracting within-dataset neighbors
|++++++++++++++ | 27% ~37m 41s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 26367 anchors
Filtering anchors
Retained 1241 anchors
Extracting within-dataset neighbors
|+++++++++++++++ | 28% ~45m 35s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 26799 anchors
Filtering anchors
Retained 1217 anchors
Extracting within-dataset neighbors
|+++++++++++++++ | 29% ~51m 59s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 10209 anchors
Filtering anchors
Retained 1230 anchors
Extracting within-dataset neighbors
|++++++++++++++++ | 31% ~51m 46s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7223 anchors
Filtering anchors
Retained 1563 anchors
Extracting within-dataset neighbors
|+++++++++++++++++ | 32% ~51m 55s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 11207 anchors
Filtering anchors
Retained 2663 anchors
Extracting within-dataset neighbors
|+++++++++++++++++ | 33% ~52m 39s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 10738 anchors
Filtering anchors
Retained 2434 anchors
Extracting within-dataset neighbors
|++++++++++++++++++ | 35% ~53m 10s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1573 anchors
Filtering anchors
Retained 662 anchors
Extracting within-dataset neighbors
|++++++++++++++++++ | 36% ~50m 55s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 26299 anchors
Filtering anchors
Retained 649 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++ | 37% ~57m 52s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 27070 anchors
Filtering anchors
Retained 434 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++ | 38% ~01h 03m 22s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 10568 anchors
Filtering anchors
Retained 843 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++ | 40% ~01h 02m 56s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 8402 anchors
Filtering anchors
Retained 1157 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++ | 41% ~01h 02m 44s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 12195 anchors
Filtering anchors
Retained 2172 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++ | 42% ~01h 03m 08s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 11602 anchors
Filtering anchors
Retained 2178 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++ | 44% ~01h 03m 19s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1662 anchors
Filtering anchors
Retained 588 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++ | 45% ~01h 00m 51s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 33458 anchors
Filtering anchors
Retained 8485 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++ | 46% ~01h 07m 37s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5079 anchors
Filtering anchors
Retained 1388 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++ | 47% ~01h 04m 49s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4935 anchors
Filtering anchors
Retained 1223 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++ | 49% ~01h 02m 05s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4470 anchors
Filtering anchors
Retained 1642 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++ | 50% ~59m 08s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4132 anchors
Filtering anchors
Retained 2217 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++ | 51% ~56m 20s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4306 anchors
Filtering anchors
Retained 2336 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++ | 53% ~53m 43s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4217 anchors
Filtering anchors
Retained 2202 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++ | 54% ~51m 12s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1367 anchors
Filtering anchors
Retained 1001 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++ | 55% ~48m 39s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4981 anchors
Filtering anchors
Retained 2589 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++ | 56% ~46m 49s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5023 anchors
Filtering anchors
Retained 3724 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++ | 58% ~45m 20s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6316 anchors
Filtering anchors
Retained 1430 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++ | 59% ~43m 27s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6133 anchors
Filtering anchors
Retained 1328 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++ | 60% ~41m 37s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5146 anchors
Filtering anchors
Retained 2252 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++ | 62% ~39m 33s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4316 anchors
Filtering anchors
Retained 2620 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++ | 63% ~37m 35s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5164 anchors
Filtering anchors
Retained 2713 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++ | 64% ~35m 43s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4944 anchors
Filtering anchors
Retained 2755 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++ | 65% ~33m 55s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1425 anchors
Filtering anchors
Retained 1123 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++ | 67% ~32m 03s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6440 anchors
Filtering anchors
Retained 4512 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++ | 68% ~30m 42s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6572 anchors
Filtering anchors
Retained 5273 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++ | 69% ~29m 35s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 3769 anchors
Filtering anchors
Retained 2991 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++ | 71% ~27m 53s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7243 anchors
Filtering anchors
Retained 1864 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++ | 72% ~26m 30s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7933 anchors
Filtering anchors
Retained 1712 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++ | 73% ~25m 07s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5622 anchors
Filtering anchors
Retained 2619 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++ | 74% ~23m 35s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4755 anchors
Filtering anchors
Retained 2521 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++ | 76% ~22m 06s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6506 anchors
Filtering anchors
Retained 3267 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20m 41s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6509 anchors
Filtering anchors
Retained 3762 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++ | 78% ~19m 19s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1588 anchors
Filtering anchors
Retained 999 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++ | 79% ~17m 54s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7063 anchors
Filtering anchors
Retained 4446 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16m 47s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 7510 anchors
Filtering anchors
Retained 5062 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~15m 47s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 3786 anchors
Filtering anchors
Retained 2505 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14m 28s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4647 anchors
Filtering anchors
Retained 3203 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13m 11s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 8904 anchors
Filtering anchors
Retained 1573 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12m 04s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 9349 anchors
Filtering anchors
Retained 1460 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10m 57s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6420 anchors
Filtering anchors
Retained 2007 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09m 45s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5370 anchors
Filtering anchors
Retained 2346 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08m 35s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6577 anchors
Filtering anchors
Retained 2921 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07m 27s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 6568 anchors
Filtering anchors
Retained 2789 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06m 20s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 1650 anchors
Filtering anchors
Retained 896 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05m 13s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 8661 anchors
Filtering anchors
Retained 3880 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04m 12s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 9081 anchors
Filtering anchors
Retained 4719 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03m 12s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4142 anchors
Filtering anchors
Retained 2302 anchors
Extracting within-dataset neighbors
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02m 06s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 4895 anchors
Filtering anchors
Retained 2969 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01m 03s
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
Found 5901 anchors
Filtering anchors
Retained 3153 anchors
Extracting within-dataset neighbors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01h 20m 31s
obj <- IntegrateData(anchorset = anchors, dims = 1:30)
Merging dataset 7 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 11 into 9
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 10 into 9 11
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 12 into 8
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 6 into 5
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 13 into 9 11 10
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 7 into 5 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 5 6 4 7 into 1 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 8 12 into 9 11 10 13
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 2 into 1 3 5 6 4 7
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 1 3 5 6 4 7 2 into 9 11 10 13 8 12
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Adding a command log without an assay associated with it
proc.time() - ptm
user system elapsed
5130.765 320.437 5458.036
obj <- ScaleData(obj, verbose = FALSE)
obj <- RunPCA(obj, npcs = 30, verbose = FALSE)
obj <- RunUMAP(obj, dims = 1:30, verbose=FALSE, reduction.name = "umap")
DimPlot(obj, reduction = "umap", group.by = "sample_new", label.size = 5, pt.size = 0) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text=element_text(size=18))
ggsave("covid_seurat_sample.pdf")
Saving 7.29 x 4.5 in image

DimPlot(obj, reduction = "umap", group.by = "sample_new", label.size = 10, pt.size = 0) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text=element_text(size=30))
ggsave("covid_seurat_sample.png", height=12, width=18, dpi = 100)

DimPlot(obj, reduction = "umap", group.by = "celltype", label = T, repel=T, label.size = 5, pt.size = 0) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text=element_text(size=18))
ggsave("covid_seurat_label.pdf")
Saving 7.29 x 4.5 in image

DimPlot(obj, reduction = "umap", group.by = "celltype", label = T, repel=T, label.size = 10, pt.size = 0) +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
text=element_text(size=30))
ggsave("covid_seurat_label.png", height=12, width=18, dpi = 100)

saveRDS(obj, "covid-seurat.RDS")
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